Error Control Research Articles

위성 항법 신호의 순방향 오류 제어 분석

위성 항법 신호의 순방향 오류 제어 분석

Decentralized event‐triggered output feedback adaptive neural network control for a class of MIMO uncertain strict‐feedback nonlinear systems with input saturation

SummaryFor a class of multiple‐input multiple‐output large‐scale nonlinear systems in strict‐feedback form with input saturation, external disturbances and immeasurable states, an adaptive decentralized neural network (NN) control strategy on the basis of event triggered mechanism is investigated in this article. In contrast to the literature, the proposed method is centered on the control‐error as a replacement to the tracking‐error that leads to a simplified derivation approach of adaptive laws. Furthermore, the control gains for this class of systems are considered unknown nonlinear functions and not assumed as simple unity or known gains as always done in the literature. Moreover, the challenge of losing controllability that typically arises in state transformation‐based methods, as reported in the literature, is entirely resolved in our approach. Last and not least, all restrictions imposed on unmatched interconnections are eliminated along with avoiding the complexity explosion caused by recursive back‐stepping designs. For this end, the unknown ideal control laws are approximated using NNs, while additional control terms are added to handle saturation effects, unknown interactions, and approximation errors. Additionally, fuzzy inference systems are employed to estimate unknown control errors. Due to the strictly positive real property, the tracking errors are proved to belong to a small compact set using Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed approach.

Measuring the performance of an artificial intelligence-based robot that classifies blood tubes and performs quality control in terms of preanalytical errors: A preliminary study.

Artificial intelligence-based robotic systems are increasingly used in medical laboratories. This study aimed to test the performance of KANKA (Labenko), a stand-alone, artificial intelligence-based robot that performs sorting and preanalytical quality control of blood tubes. KANKA is designed to perform preanalytical quality control with respect to error control and preanalytical sorting of blood tubes. To detect sorting errors and preanalytical inappropriateness within the routine work of the laboratory, a total of 1000 blood tubes were presented to the KANKA robot in 7 scenarios. These scenarios encompassed various days and runs, with 5 repetitions each, resulting in a total of 5000 instances of sorting and detection of preanalytical errors. As the gold standard, 2 experts working in the same laboratory identified and recorded the correct sorting and preanalytical errors. The success rate of KANKA was calculated for both the accurate tubes and those tubes with inappropriate identification. KANKA achieved an overall accuracy rate of 99.98% and 100% in detecting tubes with preanalytical errors. It was found that KANKA can perform the control and sorting of 311 blood tubes per hour in terms of preanalytical errors. KANKA categorizes and records problem-free tubes according to laboratory subunits while identifying and classifying tubes with preanalytical inappropriateness into the correct error sections. As a blood acceptance and tube sorting system, KANKA has the potential to save labor and enhance the quality of the preanalytical process.

Adaptive selection of the optimal strategy to improve precision and power in randomized trials.

Benkeser etal. demonstrate how adjustment for baseline covariates in randomized trials can meaningfully improve precision for a variety of outcome types. Their findings build on a long history, starting in 1932 with R.A. Fisher and including more recent endorsements by the U.S. Food and Drug Administration and the European Medicines Agency. Here, we address an important practical consideration: how to select the adjustment approach-which variables and in which form-to maximize precision, while maintaining Type-I error control. Balzer etal. previously proposed Adaptive Pre-specification within TMLE to flexibly and automatically select, from a prespecified set, the approach that maximizes empirical efficiency in small trials (N< 40). To avoid overfitting with few randomized units, selection was previously limited to working generalized linear models, adjusting for a single covariate. Now, we tailor Adaptive Pre-specification to trials with many randomized units. Using V-fold cross-validation and the estimated influence curve-squared as the loss function, we select from an expanded set of candidates, including modern machine learning methods adjusting for multiple covariates. As assessed in simulations exploring a variety of data-generating processes, our approach maintains Type-I error control (under the null) and offers substantial gains in precision-equivalent to 20%-43% reductions in sample size for the same statistical power. When applied to real data from ACTG Study 175, we also see meaningful efficiency improvements overall and within subgroups.

Fractional-Order Fuzzy PID Controller with Evolutionary Computation for an Effective Synchronized Gantry System

Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup’s recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results.

A Multichannel, Multipulse, Multiweight Block-Adaptive Quantization (3MBAQ) Algorithm Based on Space-Borne Multichannel SAR Doppler Domain A-BAQ

Amid the conflict between the current huge data volumes and the requirement for large-compression-ratio compression in space-borne multichannel SAR, this paper proposes a data compression method that uses the adaptive bit allocation BAQ method in the Doppler domain of space-borne multichannel SAR. Specifically, 3MBAQ, which denotes multichannel, multipulse, multiweight block-adaptive quantization, performs signal reconstruction through a Krieger filter to obtain the Doppler spectrum under azimuthal multichannel and multipulse conditions and then achieves nonuniform bit allocation by using the multiple weight contributions between subband spectra. Furthermore, large-compression-ratio compression can be realized in homogeneous scenarios by using the adaptive bit allocation BAQ method together with the improved bit error control (BEC) algorithm. In this paper, the effectiveness of the 3MBAQ method is verified on GF3 measured data. Moreover, compared to the SQNR and NMSE of the BAQ, A-BAQ, A-BAQ-BEC, MCBAQ-ASQ, and MCBAQ-BEC methods under the same conditions, it is shown that there is an optimal 1.3434 dB improvement in SQNR and a 0.0241 improvement in NMSE using the 3MBAQ algorithm, and the complexity of the algorithm for 3MBAQ is O(N2log(N)), which is within acceptable limits, clearly proving the superiority of the 3MBAQ method.

Comparison of Two Methods for Determining Item Characteristic Functions and Latent Variable Time-Course for Pharmacometric Item Response Models

There are examples in the literature demonstrating different approaches to defining the item characteristic functions (ICF) and characterizing the latent variable time-course within a pharmacometrics item response theory (IRT) framework. One such method estimates both the ICF and latent variable time-course simultaneously, and another method establishes the ICF first then models the latent variable directly. To date, a direct comparison of the “simultaneous” and “sequential” methodologies described in this work has not yet been systematically investigated. Item parameters from a graded response IRT model developed from Parkinson’s Progression Marker Initiative (PPMI) study data were used as simulation parameters. Each method was evaluated under the following conditions: (i) with and without drug effect and (ii) slow progression rate with smaller sample size and rapid progression rate with larger sample size. Overall, the methods performed similarly, with low bias and good precision for key parameters and hypothesis testing for drug effect. The ICF parameters were well determined when the model was correctly specified, with an increase in precision in the scenario with rapid progression. In terms of drug effect, both methods had large estimation bias for the slow progression rate; however, this bias can be considered small relative to overall progression rate. Both methods demonstrated type 1 error control and similar discrimination between model with and without drug effect. The simultaneous method was slightly more precise than the sequential method while the sequential method was more robust towards longitudinal model misspecification and offers practical advantages in model building.

Advancing Process Control in Fluidized Bed Biomass Gasification Using Model-Based Deep Reinforcement Learning

This study presents a model-based deep reinforcement learning (MB-DRL) controller for the fluidized bed biomass gasification (FBG) process. The MB-DRL controller integrates a deep neural network (DNN) model and a reinforcement learning-based optimizer. The DNN model is trained with operational data from a pilot-scale FBG plant to approximate FBG process dynamics. The reinforcement learning-based optimizer employs a specially designed reward function, determining optimal control policies for FBG. Moreover, the controller includes an online learning component, ensuring periodic updates to the DNN model training. The performance of the controller is evaluated by testing its control accuracy for regulating synthetic gas composition, flow rate, and CO concentration in the FBG. The evaluation also includes a comparison with a model predictive controller. The results demonstrate the superior control performance of MB-DRL, surpassing MPC by over 15% in regulating synthetic gas composition and flow rate, with similar effectiveness observed in synthetic gas temperature control. Additionally, this study also includes systematic investigations into factors like DNN layer count and learning update intervals to provide insights for the practical implementation of the controller. The results, presenting a 50% reduction in control error with the addition of a single layer to the DNN model, highlight the significance of optimizing MB-DRL for effective implementation.

Retracted: Research on Mapping Error Control of Underground Space Mobile LiDAR Constrained by Cooperative Targets

Retracted: Research on Mapping Error Control of Underground Space Mobile LiDAR Constrained by Cooperative Targets

Optical transmission of microwave control signal towards large-scale superconducting quantum computing.

With the rapid development of superconducting quantum computing and the implementation of surface code, large-scale quantum computing is emerging as an urgent demand. In a superconducting computing system, the qubit is maintained in a cryogenic environment to avoid thermal excitation. Thus, the transmission of control signals, which are generated at room temperature, is needed. Typically, the transmission of these signals to the qubit relies on a coaxial cable wiring approach. However, in a large-scale computing system with hundreds or even thousands of qubits, the coaxial cables will pose great space and heat load to the dilution refrigerator. Here, to tackle this problem, we propose and demonstrate a direct-modulation-based optical transmission line. In our experiment, the average single-qubit XEB error and control error are measured as 0.139% and 0.014% separately, demonstrating the feasibility of the optical wiring approach and paving the way for large-scale superconducting quantum computing.

Robust adaptive iterative learning control for high-speed trains under non-strictly repeated conditions

In this paper, the speed and position control of high-speed trains (HSTs) under non-strictly repeated conditions is investigated. Considering the modeling accuracy and control cost, a multi-particle dynamics model is established in a single coordinate system. Based on this model, a novel robust adaptive iterative learning control (RAILC) method is proposed. The Lyapunov function is established to prove that the system state converges in a finite time, and the composite energy function (CEF) is constructed to prove that the control error approaches zero along the iteration axis. The application value in engineering practice of the proposed RAILC is verified by two simulation tests. Existing adaptive iterative learning control (AILC) and adaptive terminal sliding mode control (ATSMC) are introduced as control groups to show the system convergence of the RAILC on the iteration axis and time axis.

Enhancing four-axis machining center accuracy through interactive fusion of spatiotemporal graph convolutional networks and an error-controlled digital twin system

Mitigating thermal errors constitutes a crucial method for enhancing the machining accuracy of four-axis machining centers. At the heart of effective thermal error control lie the thermal error control platform and a resilient thermal error prediction model. It is imperative to note that thermal errors exhibit intricate dynamic and nonlinear spatiotemporal dependencies. However, prevailing thermal error prediction models tend to primarily focus on temporal features or employ simplistic spatiotemporal characteristics, resulting in diminished accuracy and robustness. Furthermore, the present thermal error compensation system is plagued by a lack of user-friendliness, stemming from its suboptimal execution efficiency. In response to the aforementioned challenges, an innovative approach: the interactive fusion spatiotemporal graph convolutional network is proposed. This novel model is specifically designed to capture the intricate dynamic spatiotemporal dependencies inherent in thermal errors. The interactive fusion spatiotemporal graph convolutional network model consists of three essential components: a bilinear temporal convolutional network, a multi-layer spatiotemporal module, and a linear module. These components work in harmony to comprehensively extract both global and local spatiotemporal features. Subsequently, a mapping relationship between thermal errors and compensation components is established, laying the foundation for theoretical advancements in thermal error compensation within the realm of four-axis machining centers. A digital twin system framework tailored for error control is devised, which leverages cloud-edge computing to enable dynamic control and real-time monitoring of thermal errors. To assess the effectiveness of this digital twin system framework and the interactive fusion spatiotemporal graph convolutional network model, a series of rigorous experiments were conducted. The oriented to error-controlled digital twin system coupled with the interactive fusion spatiotemporal graph convolutional network model yielded exceptional machining accuracy, resulting in minimal geometric disparities of [−3.0 μm, 3.0 μm] for the central hole diameter D and [−3.5 μm, 4.0 μm] for the hole distance H.

Over-sampling for data augmentation in data-driven models for the shear strength prediction of RC membranes

Complex reinforced concrete (RC) structures are generally assessed as a group of individual membrane elements subjected to in-plane combined stresses; however, an accurate prediction of the shear strength of such elements is still a complex task. In addition, the limited availability of experimental data of RC panels, which also presents an unbalanced statistical distribution towards lower strength values, limits the development of data-driven models. Thus, it is crucial to explore data augmentation techniques with a view to supporting the development of more accurate and generalizable predictive models in structural engineering. This paper evaluates over-sampling techniques for data augmentation and their use in the creation of an explainable, data-driven model for the shear strength prediction of RC panels. A dataset of 195 experimental tests of RC panels under different loading conditions is initially collected. Five over-sampling techniques are implemented to extend the original dataset and to reduce the imbalance. Three ensemble models (Random Forest, AdaBoost, and XGBoost) are trained with each of the generated datasets. It is observed that all the over-sampling techniques produced predictive models with better performance than the original dataset; however, the results show that by applying the Random Over-Sampling (ROS) the performance metrics of the model can significantly increase (around 39% for some metrics) compared to the model with the original dataset, without any overfitting issues. This strategy allowed to develop an accurate XGBoost model (with a value of R2 = 0.97 for the testing set). The explainability of the final predictive model (XGBoost model obtained from ROS) is evaluated using the SHAP (SHapley Additive exPlanations) analysis. The proposed predictive model outperformed traditional mechanics-based models (improvement of approximately 27% over SMCS and 33% over MCFT for some performance metrics) and with a more controlled error distribution over the range of variables. The proposed model was also more accurate (mean prediction ratio of 0.98) than sophisticated finite element analysis (mean prediction ratio of 0.84) for six specimens of the original dataset.

Estimate exponential memory decay in hidden Markov model and its applications to inference

Inference in hidden Markov model has been challenging in terms of scalability due to dependencies in the observation data. In this paper, we utilize the inherent memory decay in hidden Markov models, such that the forward and backward probabilities can be carried out with subsequences, enabling efficient inference over long sequences of observations. We formulate this forward filtering process in the setting of the random dynamical system and there exist Lyapunov exponents in the i.i.d random matrices production. And the rate of the memory decay is known as λ2−λ1, the gap of the top two Lyapunov exponents almost surely. An efficient and accurate algorithm is proposed to numerically estimate the gap after the soft-max parametrization. The length of subsequences B given the controlled error ϵ is B≈log(ϵ)/(λ2−λ1). We theoretically prove the validity of the algorithm and demonstrate the effectiveness with numerical examples. The method developed here can be applied to widely used algorithms, such as mini-batch stochastic gradient method. Moreover, the continuity of Lyapunov spectrum ensures the estimated B could be reused for the nearby parameter during the inference.

Analysis of the Control Characteristics of the Electro-Hydraulic Vibration System Based on the Single-Neuron Control Algorithm

This paper proposes an electro-hydraulic vibration control system based on the single-neuron PID algorithm, which improves the operating frequency of the electro-hydraulic fatigue testing machine and the control accuracy of the load force. Through mathematical modeling of the electro-hydraulic vibration system (EVS), a MATLAB/Simulink simulation, and experimental testing, this study systematically analyzes the output waveform of the EVS as well as the closed-loop situation of load force amplitude and offset under the action of the single-neuron PID algorithm. The results show that: the EVS with a 2D vibration valve as the core, which can control the movement of the spool in the two-degrees-of-freedom direction, can realize the output of an approximate sinusoidal load force waveform from 0 to 800 Hz. The system controlled by the single-neuron PID algorithm is less complex to operate than the traditional PID algorithm. It also has a short rise time for the output load force amplitude curve and a maximum control error of only 1.2%. Furthermore, it exhibits a rapid closed-loop response to the load force offset. The range variability of the load force is measured to be 1.43%. A new scheme for the design of EVS is provided in this study, which broadens the application range of electro-hydraulic fatigue testing machines.

Application of self-improving Q-learning controller for a class of dynamical processes: Implementation aspects

This paper deals with a practical application of the Q-learning algorithm as a general-purpose self-improving controller operating in a class of industrial closed-loop control systems. The proposed approach solves a practical control engineering problem by designing a controller that starts its operation with the predefined closed-loop control performance and then, without unnecessarily disturbing the normally operated closed-loop system, it learns on-line from interactions with the controlled process to ensure the gradual improvement in the closed loop control performance until this performance reaches a desired user-defined level. It is proposed how to ensure its initial performance by an appropriate initialization of the Q-matrix without assuming any knowledge about the process model. The desired target closed-loop performance is defined by the first-order reference trajectory that should be preserved by the closed-loop system, which requires determining the current state of the closed-loop system by the values of the control error and its time derivative. The novelty of the proposed approach results from: (i) preserving the first order reference trajectory with reduced dimensions of the Q-matrix to two, which significantly reduces memory and computational requirements without any loss of generality, (ii) proposing a method for a reduction of the number of states designed directly for the proposed approach, and (iii) proposing a method for a very convenient initialization of the Q-matrix based only on the tunings of the existing PI controller. All these features bring the proposed approach closer towards practical applications. Results of the simulation and experimental validation show that the proposed Q-learning controller can substitute the existing PI controller bumplessly and provides a gradual improvement in the closed loop performance due to its online learning abilities.

Robustness of quantum algorithms against coherent control errors

Robustness of quantum algorithms against coherent control errors

Self-organizing model reference adaptive control for aircraft with enhanced persistent excitation

Model Reference Adaptive Control (MRAC) methods are commonly applied in aerospace control systems, yet they face challenges in terms of persistent excitation, adaptation speed, and network structure design. To tackle these issues, this study introduces a novel MRAC method that employs practical techniques, including fast adaptation, experience replay, and network self-organization. Specifically, this study emphasizes three key contributions. First, it introduces a modified MRAC architecture that incorporates a fast adaptation term and a network approximation term to compensate for control errors more effectively. The fast adaptation term, which exhibits higher adaptive speed, enables quick response to dynamic variations and is stored in an experience replay buffer. Additionally, the network term is trained using batches of past samples extracted from the experience replay buffer, rather than relying solely on the current sample. This approach successfully overcomes challenges related to achieving persistent excitation and setting adaptation speed. Secondly, this paper introduces an online method for network self-organization to approximate a global model. This approach autonomously selects basis functions to enhance the organization of the network, leading to improved matching and overall performance. Finally, theoretical proofs and numerical simulations are presented to validate the effectiveness of the proposed techniques and demonstrate the advantages of the method in terms of parameter convergence, learning speed, and control stability.

Dual closed-loop control method for resonant integrated optic gyroscopes with combination differential modulation

In order to optimize the detection accuracy and output stability of Resonant Integrated Optic Gyroscopes (RIOG), a dual closed-loop control method for combined differential modulation (DCM Control) has been proposed. Two optical signals are differentially modulated in sync, and their differential-mode output results are compensated in real-time. Considering the distinct characteristics of the two frequency servo loops, they are respectively utilized to tune the laser frequency to track the resonant frequency's wide fluctuations and continuously changing resonant frequency due to random rotation. Through this novel structure, we have enhanced reciprocity while reducing optical components, suppressing RIM noise and laser frequency noise in the gyroscope system, and effectively reducing control errors. We analyze the principles affecting angular velocity detection accuracy and the system's output model. Experimental results indicate that this method can effectively achieve high reciprocity and high-precision frequency control, offering a larger dynamic detection range and better linearity. The long-term bias instability (BI) based on Allan deviation is measured at 0.95°/h, and the angle random walk (ARW) is 0.119°/√h, providing a simple and efficient approach for RIOG closed-loop signal processing.

Trajectory tracking control of autonomous vehicles based on Lagrangian neural network dynamics model

The autonomous vehicles make decisions and plans based on the environmental perception and generate the target command of the control layer. The vehicle dynamics model is an important factor that affects the vehicle control. The dynamic mechanism model has strong interpretability and good stability. However, in extreme conditions, the model accuracy is reduced due to the tire entering the nonlinear region. The data-driven dynamic model achieves high modeling accuracy. However, due to the lack of physical constraints and rationality in the data-driven models, the interpretability and stability of the control is reduced, which in turn increases the unpredictable risk in the driving process. This paper innovatively proposes a deep Lagrangian neural network dynamics model (DeLaN) for autonomous vehicles based on the Lagrangian mechanics and uses a neural network to encode the differential equations. This not only retains the interpretability of the physical model but also makes full use of the learning ability and fitting ability of the neural network to effectively capture the complex dynamic characteristics of the vehicle. To improve the robustness of the control system, this work uses DeLaN as feed-forward control and preview error feedback control to form a closed loop of trajectory tracking control for autonomous vehicles. The experimental results show that the trajectory tracking error of the proposed DeLaN is significantly reduced, the yaw stability and comfort are significantly improved, good longitudinal and lateral cooperative control performance is achieved, and the physical rationality of the neural network is also improved. Therefore, the proposed DeLaN has important engineering application value.